The present invention relates generally to optical-digital signal processing systems, and, in particular, to systems that extract features from optical images for pattern recognition.
Optics and pattern recognition are key areas for systems development for many applications, including tactical missile guidance, strategic surveillance, optical parts inspection, medical imaging and non-destructive evaluation. Both passive imaging sensors (infrared (IR) and visible) and active microwave imaging sensors have been employed in many systems to date, but pattern recognition solutions in conjunction with these sensors are highly application dependent and have required extensive training. These factors have precluded extensive development.
Pattern recognition using imaging sensors can be implemented by means of feature extraction. Generally speaking, optical processing offers a fast and highly parallel method of feature extraction and correlation using the fundamental properties of wavefront multiplication, addition, rotation, and splitting. On the other hand, one of the key concerns in the design of optical feature extractors is that the features selected for pattern recognition should be invariant with respect to position, scale, and rotation, which simple image correlators are particularly sensitive to in most cases. Furthermore, traditional approaches have involved complicated mathematical transformations to achieve distortion invariance.
As will be described in more detail below, the invention offers a compact, distortion-insensitive method of optical feature extraction using primitive image operations such as image replication, multiplication, integration, and detection, and is useful in viewing objects in plan-view. One of the key aspects of this approach is the use of optical feature extraction to measure objects rather than match them. Matching is left to a neural network.